Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity

Abstract

Environmental factors are the largest contributors to cardiovascular disease. Here we show that cardiac organoids that incorporate an oxygen-diffusion gradient and that are stimulated with the neurotransmitter noradrenaline model the structure of the human heart after myocardial infarction (by mimicking the infarcted, border and remote zones), and recapitulate hallmarks of myocardial infarction (in particular, pathological metabolic shifts, fibrosis and calcium handling) at the transcriptomic, structural and functional levels. We also show that the organoids can model hypoxia-enhanced doxorubicin cardiotoxicity. Human organoids that model diseases with non-genetic pathological factors could help with drug screening and development.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Development of human 3D post-MI organoid model.
Fig. 2: Human in vitro 3D post-MI organoids share a global gene-expression profile with adult human ICM and animal acute post-MI samples.
Fig. 3: Pathological metabolic responses of the cardiac infarct organoids model at the transcriptomic, functional and tissue levels.
Fig. 4: Pathological fibrosis responses of the cardiac infarct organoids model at the transcriptomic, cellular and tissue level.
Fig. 5: Tissue-level pathological calcium handling in cardiac infarct organoids observed with in situ imaging of the interior of live cardiac organoids.
Fig. 6: Human cardiac infarct organoids for tissue-level heart-failure drug testing.
Fig. 7: Detection of tissue-level drug-induced exacerbation of cardiotoxicity using cardiac infarct organoids.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are available from the corresponding authors on reasonable request. RNA-seq data are available from the NCBI GEO, under the accession numbers GSE113871 and GSE115031.

Code availability

Custom LabVIEW codes for controlling the custom-built 2PLSM are available from the corresponding author on reasonable request.

References

  1. Chamberlain, S. J. Disease modelling using human iPSCs. Hum. Mol. Genet. 25, R173–R181 (2016).

    CAS  PubMed  Google Scholar 

  2. Inoue, H., Nagata, N., Kurokawa, H. & Yamanaka, S. iPS cells: a game changer for future medicine. EMBO J. 33, 409–417 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    CAS  PubMed  Google Scholar 

  4. Fatehullah, A., Tan, S. H. & Barker, N. Organoids as an in vitro model of human development and disease. Nat. Cell Biol. 18, 246–254 (2016).

    PubMed  Google Scholar 

  5. Lancaster, M. A. & Knoblich, J. A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).

    PubMed  Google Scholar 

  6. Driehuis, E. & Clevers, H. CRISPR/Cas 9 genome editing and its applications in organoids. Am. J. Physiol. Gastrointest. Liver Physiol. 312, G257–G265 (2017).

    PubMed  Google Scholar 

  7. Nie, J. & Hashino, E. Organoid technologies meet genome engineering. EMBO Rep. 18, 367–376 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Di Lullo, E. & Kriegstein, A. R. The use of brain organoids to investigate neural development and disease. Nat. Rev. Neurosci. 18, 573–584 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Drost, J. & Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 18, 407–418 (2018).

    CAS  PubMed  Google Scholar 

  10. Noordhoek, J., Gulmans, V., van der Ent, K. & Beekman, J. M. Intestinal organoids and personalized medicine in cystic fibrosis: a successful patient-oriented research collaboration. Curr. Opin. Pulm. Med. 22, 610–616 (2016).

    PubMed  Google Scholar 

  11. Nantasanti, S., de Bruin, A., Rothuizen, J., Penning, L. C. & Schotanus, B. A. Concise review: organoids are a powerful tool for the study of liver disease and personalized treatment design in humans and animals. Stem Cells Transl. Med. 5, 325–330 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Benjamin, E. J. et al. Heart disease and stroke statistics—2017 Update: a report from the American Heart Association. Circulation 135, e146–e603 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. Tiburcy, M. et al. Defined engineered human myocardium with advanced maturation for applications in heart failure modeling and repair. Circulation 135, 1832–1847 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang, D. et al. Tissue-engineered cardiac patch for advanced functional maturation of human ESC-derived cardiomyocytes. Biomaterials 34, 5813–5820 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Giacomelli, E. et al. Three-dimensional cardiac microtissues composed of cardiomyocytes and endothelial cells co-differentiated from human pluripotent stem cells. Development 144, 1008–1017 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Ulmer, B. M. et al. Contractile work contributes to maturation of energy metabolism in hiPSC-derived cardiomyocytes. Stem Cell Rep. 10, 834–847 (2018).

    CAS  Google Scholar 

  17. Mills, R. J. et al. Functional screening in human cardiac organoids reveals a metabolic mechanism for cardiomyocyte cell cycle arrest. Proc. Natl Acad. Sci. USA 114, E8372–E8381 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Liang, P. et al. Drug screening using a library of human induced pluripotent stem cell-derived cardiomyocytes reveals disease-specific patterns of cardiotoxicity. Circulation 127, 1677–1691 (2013).

    CAS  PubMed  Google Scholar 

  19. Dell’Era, P. et al. Cardiac disease modeling using induced pluripotent stem cell-derived human cardiomyocytes. World J. Stem Cells 7, 329–342 (2015).

    PubMed  PubMed Central  Google Scholar 

  20. Matsa, E. et al. Transcriptome profiling of patient-specific human iPSC-cardiomyocytes predicts individual drug safety and efficacy responses in vitro. Cell Stem Cell 19, 311–325 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Burridge, P. W. et al. Human induced pluripotent stem cell-derived cardiomyocytes recapitulate the predilection of breast cancer patients to doxorubicin-induced cardiotoxicity. Nat. Med. 22, 547–556 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang, G. et al. Modeling the mitochondrial cardiomyopathy of Barth syndrome with induced pluripotent stem cell and heart-on-chip technologies. Nat. Med. 20, 616–623 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ma, Z. et al. Contractile deficits in engineered cardiac microtissues as a result of MYBPC3 deficiency and mechanical overload. Nat. Biomed. Eng. 2, 955–967 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Yusuf, S. A 35-year journey to evidence-based medicine: a personal story. Eur. Heart J. 36, 3460–3466 (2015).

    PubMed  Google Scholar 

  25. Gheorghiade, M. et al. Developing new treatments for heart failure: focus on the heart. Circ. Heart Fail. 9, e002727 (2016).

  26. Fine, B. & Vunjak-Novakovic, G. Shortcomings of animal models and the rise of engineered human cardiac tissue. ACS Biomater. Sci. Eng. 3, 1884–1897 (2017).

    CAS  PubMed  Google Scholar 

  27. Kaye, D. M. & Krum, H. Drug discovery for heart failure: a new era or the end of the pipeline? Nat. Rev. Drug Discov. 6, 127–139 (2007).

    CAS  PubMed  Google Scholar 

  28. Prat-Vidal, C. et al. Identification of temporal and region-specific myocardial gene expression patterns in response to infarction in swine. PLoS ONE 8, e54785 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Tarnavski, O. et al. Mouse cardiac surgery: comprehensive techniques for the generation of mouse models of human diseases and their application for genomic studies. Physiol. Genom. 16, 349–360 (2004).

    CAS  Google Scholar 

  30. Chen, T. & Vunjak-Novakovic, G. Human tissue-engineered model of myocardial ischemia-reperfusion injury. Tissue Eng. Part A 25, 711–724 (2018).

    PubMed  Google Scholar 

  31. Ugolini, G. S. et al. Human cardiac fibroblasts adaptive responses to controlled combined mechanical strain and oxygen changes in vitro. eLife 6, e22847 (2017).

    PubMed  PubMed Central  Google Scholar 

  32. Lowes, B. D. et al. Serial gene expression profiling in the intact human heart. J. Heart Lung Transplant 25, 579–588 (2006).

    PubMed  PubMed Central  Google Scholar 

  33. Stevens, J. L. & Baker, T. K. The future of drug safety testing: expanding the view and narrowing the focus. Drug Discov. Today 14, 162–167 (2009).

    PubMed  Google Scholar 

  34. Horvath, P. et al. Screening out irrelevant cell-based models of disease. Nat. Rev. Drug Discov. 15, 751–769 (2016).

    CAS  PubMed  Google Scholar 

  35. Page, R. L. II et al. Drugs that may cause or exacerbate heart failure: a scientific statement from the American Heart Association. Circulation 134, e32–e69 (2016).

    CAS  PubMed  Google Scholar 

  36. Nunes, S. S. et al. Human stem cell-derived cardiac model of chronic drug exposure. ACS Biomater. Sci. Eng. 3, 1911–1921(2016).

  37. Occhetta, P. et al. A three-dimensional in vitro dynamic micro-tissue model of cardiac scar formation. Integr. Biol. 10, 174–183 (2018).

    CAS  Google Scholar 

  38. Sadeghi, A. H. et al. Engineered 3D cardiac fibrotic tissue to study fibrotic remodeling. Adv. Healthc. Mater. 6, 1601434 (2017).

  39. van Spreeuwel, A. C. C. et al. Mimicking cardiac fibrosis in a dish: fibroblast density rather than collagen density weakens cardiomyocyte function. J. Cardiovasc. Transl. Res. 10, 116–127 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. Zhao, H. et al. Microengineered in vitro model of cardiac fibrosis through modulating myofibroblast mechanotransduction. Biofabrication 6, 045009 (2014).

    PubMed  Google Scholar 

  41. Radisic, M. et al. Oxygen gradients correlate with cell density and cell viability in engineered cardiac tissue. Biotechnol. Bioeng. 93, 332–343 (2006).

    CAS  PubMed  Google Scholar 

  42. Lymperopoulos, A., Rengo, G. & Koch, W. J. Adrenergic nervous system in heart failure: pathophysiology and therapy. Circ. Res. 113, 739–753 (2013).

    CAS  PubMed  Google Scholar 

  43. Richards, D. J. et al. Inspiration from heart development: biomimetic development of functional human cardiac organoids. Biomaterials 142, 112–123 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Frangogiannis, N. G. Pathophysiology of myocardial Infarction. Compr. Physiol. 5, 1841–1875 (2015).

    PubMed  Google Scholar 

  45. Brown, D. A. et al. Analysis of oxygen transport in a diffusion-limited model of engineered heart tissue. Biotechnol. Bioeng. 97, 962–975 (2007).

    CAS  PubMed  Google Scholar 

  46. Davis, B. H. et al. Effects of myocardial infarction on the distribution and transport of nutrients and oxygen in porcine myocardium. J. Biomech. Eng. 134, 101005 (2012).

    PubMed  Google Scholar 

  47. Semenza, G. L. Hypoxia-inducible factor 1: regulator of mitochondrial metabolism and mediator of ischemic preconditioning. Biochim. Biophys. Acta 1813, 1263–1268 (2011).

    CAS  PubMed  Google Scholar 

  48. Beeson, C. C., Beeson, G. C. & Schnellmann, R. G. A high-throughput respirometric assay for mitochondrial biogenesis and toxicity. Anal. Biochem. 404, 75–81 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Graham, R. M. et al. A unique pathway of cardiac myocyte death caused by hypoxia-acidosis. J. Exp. Biol. 207, 3189–3200 (2004).

    CAS  PubMed  Google Scholar 

  50. Kligfield, P., Horner, H. & Brachfeld, N. A model of graded ischemia in the isolated perfused rat heart. J. Appl. Physiol. 40, 1004–1008 (1976).

    CAS  PubMed  Google Scholar 

  51. Valvona, C. J., Fillmore, H. L., Nunn, P. B. & Pilkington, G. J. The regulation and function of lactate dehydrogenase a: therapeutic potential in brain tumor. Brain Pathol. 26, 3–17 (2016).

    CAS  PubMed  Google Scholar 

  52. Bonen, A. Lactate transporters (MCT proteins) in heart and skeletal muscles. Med. Sci. Sports Exerc. 32, 778–789 (2000).

    CAS  PubMed  Google Scholar 

  53. Draoui, N. & Feron, O. Lactate shuttles at a glance: from physiological paradigms to anti-cancer treatments. Dis. Model. Mech. 4, 727–732 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Chen, W. & Frangogiannis, N. G. Fibroblasts in post-infarction inflammation and cardiac repair. Biochim. Biophys. Acta 1833, 945–953 (2013).

    CAS  PubMed  Google Scholar 

  55. van den Borne, S. W. et al. Molecular imaging of interstitial alterations in remodeling myocardium after myocardial infarction. J. Am. Coll. Cardiol. 52, 2017–2028 (2008).

    PubMed  Google Scholar 

  56. Mewton, N., Liu, C. Y., Croisille, P., Bluemke, D. & Lima, J. A. Assessment of myocardial fibrosis with cardiovascular magnetic resonance. J. Am. Coll. Cardiol. 57, 891–903 (2011).

    PubMed  Google Scholar 

  57. Messroghli, D. R. et al. Myocardial T1 mapping: application to patients with acute and chronic myocardial infarction. Magn. Reson. Med. 58, 34–40 (2007).

    PubMed  Google Scholar 

  58. Ho, C. Y. et al. Myocardial fibrosis as an early manifestation of hypertrophic cardiomyopathy. N. Engl. J. Med. 363, 552–563 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Weber, K. T. et al. Collagen remodeling of the pressure-overloaded, hypertrophied nonhuman primate myocardium. Circ. Res. 62, 757–765 (1988).

    CAS  PubMed  Google Scholar 

  60. Yong, K. W. et al. Mechanoregulation of cardiac myofibroblast differentiation: implications for cardiac fibrosis and therapy. Am. J. Physiol. Heart Circ. Physiol. 309, H532–H542 (2015).

    CAS  PubMed  Google Scholar 

  61. Trickey, W. R., Lee, G. M. & Guilak, F. Viscoelastic properties of chondrocytes from normal and osteoarthritic human cartilage. J. Orthop. Res. 18, 891–898 (2000).

    CAS  PubMed  Google Scholar 

  62. Richardson, W. J., Clarke, S. A., Quinn, T. A. & Holmes, J. W. Physiological implications of myocardial scar structure. Compr. Physiol. 5, 1877–1909 (2015).

    PubMed  PubMed Central  Google Scholar 

  63. Herum, K. M., Choppe, J., Kumar, A., Engler, A. J. & McCulloch, A. D. Mechanical regulation of cardiac fibroblast profibrotic phenotypes. Mol. Biol. Cell 28, 1871–1882 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Lou, Q., Janardhan, A. & Efimov, I. R. Remodeling of calcium handling in human heart failure. Adv. Exp. Med. Biol. 740, 1145–1174 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Bhar-Amato, J., Davies, W. & Agarwal, S. Ventricular arrhythmia after acute myocardial infarction: ‘the perfect storm’. Arrhythm. Electrophysiol. Rev. 6, 134–139 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. Huisken, J. & Stainier, D. Y. Selective plane illumination microscopy techniques in developmental biology. Development 136, 1963–1975 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Truong, T. V., Supatto, W., Koos, D. S., Choi, J. M. & Fraser, S. E. Deep and fast live imaging with two-photon scanned light-sheet microscopy. Nat. Methods 8, 757–760 (2011).

    CAS  PubMed  Google Scholar 

  68. Morita, N., Mandel, W. J., Kobayashi, Y. & Karagueuzian, H. S. Cardiac fibrosis as a determinant of ventricular tachyarrhythmias. J. Arrhythm. 30, 389–394 (2014).

    PubMed  Google Scholar 

  69. Duan, Q. et al. BET bromodomain inhibition suppresses innate inflammatory and profibrotic transcriptional networks in heart failure. Sci. Transl. Med. 9, eaah5084 (2017).

  70. Mehta, L. S. et al. Cardiovascular disease and breast cancer: where these entities intersect: a scientific statement from the American Heart Association. Circulation 137, e30–e66 (2018).

    PubMed  PubMed Central  Google Scholar 

  71. Patnaik, J. L., Byers, T., DiGuiseppi, C., Dabelea, D. & Denberg, T. D. Cardiovascular disease competes with breast cancer as the leading cause of death for older females diagnosed with breast cancer: a retrospective cohort study. Breast Cancer Res. 13, R64 (2011).

    PubMed  PubMed Central  Google Scholar 

  72. Salz, T. et al. Preexisting cardiovascular risk and subsequent heart failure among non-Hodgkin lymphoma survivors. J. Clin. Oncol. 35, 3837–3843 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Narayan, H. K. et al. Detailed echocardiographic phenotyping in breast cancer patients: associations with ejection fraction decline, recovery, and heart failure symptoms over 3 years of follow-up. Circulation 135, 1397–1412 (2017).

    PubMed  PubMed Central  Google Scholar 

  74. Arafa, M. H., Mohammad, N. S., Atteia, H. H. & Abd-Elaziz, H. R. Protective effect of resveratrol against doxorubicin-induced cardiac toxicity and fibrosis in male experimental rats. J. Physiol. Biochem. 70, 701–711 (2014).

    CAS  PubMed  Google Scholar 

  75. Torti, F. M. et al. Cardiotoxicity of epirubicin and doxorubicin: assessment by endomyocardial biopsy. Cancer Res. 46, 3722–3727 (1986).

    CAS  PubMed  Google Scholar 

  76. Mortensen, S. A., Olsen, H. S. & Baandrup, U. Chronic anthracycline cardiotoxicity: haemodynamic and histopathological manifestations suggesting a restrictive endomyocardial disease. Br. Heart J. 55, 274–282 (1986).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Tham, E. B. et al. Diffuse myocardial fibrosis by T1-mapping in children with subclinical anthracycline cardiotoxicity: relationship to exercise capacity, cumulative dose and remodeling. J. Cardiovasc. Magn. Reson. 15, 48 (2013).

    PubMed  PubMed Central  Google Scholar 

  78. Zamorano, J. L. et al. 2016 ESC Position Paper on cancer treatments and cardiovascular toxicity developed under the auspices of the ESC Committee for Practice Guidelines: the Task Force for cancer treatments and cardiovascular toxicity of the European Society of Cardiology (ESC). Eur. Heart J. 37, 2768–2801 (2016).

    PubMed  Google Scholar 

  79. Stevens, K. R. & Murry, C. E. Human pluripotent stem cell-derived engineered tissues: clinical considerations. Cell Stem Cell 22, 294–297 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Forte, E., Furtado, M. B. & Rosenthal, N. The interstitium in cardiac repair: role of the immune-stromal cell interplay. Nat. Rev. Cardiol. 15, 601–616 (2018).

    CAS  PubMed  Google Scholar 

  81. Leopold, J. A. & Loscalzo, J. Emerging role of precision medicine in cardiovascular disease. Circ. Res. 122, 1302–1315 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Santolini, M. et al. A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure. NPJ Syst. Biol. Appl. 4, 12 (2018).

    PubMed  PubMed Central  Google Scholar 

  83. van den Heuvel, N. H., van Veen, T. A., Lim, B. & Jonsson, M. K. Lessons from the heart: mirroring electrophysiological characteristics during cardiac development to in vitro differentiation of stem cell derived cardiomyocytes. J. Mol. Cell. Cardiol. 67, 12–25 (2014).

    PubMed  Google Scholar 

  84. Davis-Turak, J. et al. Genomics pipelines and data integration: challenges and opportunities in the research setting. Expert Rev. Mol. Diagn. 17, 225–237 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  86. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    PubMed  PubMed Central  Google Scholar 

  87. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  Google Scholar 

  89. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  90. Draghici, S. et al. A systems biology approach for pathway level analysis. Genome Res. 17, 1537–1545 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Hannenhalli, S. et al. Transcriptional genomics associates FOX transcription factors with human heart failure. Circulation 114, 1269–1276 (2006).

    CAS  PubMed  Google Scholar 

  92. Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 1–25 (2004).

    Google Scholar 

  93. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  94. Davis, S. & Meltzer, P. S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23, 1846–1847 (2007).

    PubMed  Google Scholar 

  95. Jol, S. J. Make a Venn diagram stefanjol.nl https://www.stefanjol.nl/venny (2015).

  96. Ignatchenko, V., Ignatchenko, A., Sinha, A., Boutros, P. C. & Kislinger, T. VennDIS: a JavaFX-based Venn and Euler diagram software to generate publication quality figures. Proteomics 15, 1239–1244 (2015).

    CAS  PubMed  Google Scholar 

  97. Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Yang, K. C. et al. Deep RNA sequencing reveals dynamic regulation of myocardial noncoding RNAs in failing human heart and remodeling with mechanical circulatory support. Circulation 129, 1009–1021 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Ounzain, S. et al. Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs. Eur. Heart J. 36, 353–368 (2015).

    CAS  PubMed  Google Scholar 

  100. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Huang, S., Heikal, A. A. & Webb, W. W. Two-photon fluorescence spectroscopy and microscopy of NAD(P)H and flavoprotein. Biophys. J. 82, 2811–2825 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Russell, S., Wojtkowiak, J., Neilson, A. & Gillies, R. J. Metabolic profiling of healthy and cancerous tissues in 2D and 3D. Sci. Rep. 7, 15285 (2017).

    PubMed  PubMed Central  Google Scholar 

  103. Sato, M., Theret, D. P., Wheeler, L. T., Ohshima, N. & Nerem, R. M. Application of the micropipette technique to the measurement of cultured porcine aortic endothelial cell viscoelastic properties. J. Biomech. Eng. 112, 263–268 (1990).

    CAS  PubMed  Google Scholar 

  104. Theret, D. P., Levesque, M. J., Sato, M., Nerem, R. M. & Wheeler, L. T. The application of a homogeneous half-space model in the analysis of endothelial cell micropipette measurements. J. Biomech. Eng. 110, 190–199 (1988).

    CAS  PubMed  Google Scholar 

  105. Edelstein, A. D. et al. Advanced methods of microscope control using muManager software. J. Biol. Methods 1, e10 (2014).

Download references

Acknowledgements

We thank W. da Silveira for insights into microarray, RNA-seq and GSEA analysis and the staff of the laboratory of M. Morad for help with GCaMP6 labelling. The work was supported by the National Institutes of Health (R01 HL133308, 8P20 GM103444, U54 GM104941), National Institute of General Medical Sciences (P20GM-103499), start-up funds from Clemson University, the National Science Foundation (NSF; EPS-0903795, 1539034), the NIH Cardiovascular Training Grant (T32 HL007260), SCTR Institute CTSA NIH/NCATS (UL1TR001450) and US Department of Veterans Affairs Merit Review (I01 BX002327); and NIH grants (R03 DE018741 and R01 DE021134 to H.Y). G.H. acknowledges support from NIH/NIDA (1U01DA045300-01A1). This study used the services of the Morphology, Imaging and Instrumentation Core, which is supported by NIH-NIGMS P30 GM103342 to the South Carolina COBRE for Developmentally Based Cardiovascular Diseases and was supported in part by the Genomics Shared Resource, Hollings Cancer Center, and the Medical University of South Carolina (P30 CA138313). The Bioenergetics Profiling Core is supported by the COBRE in Redox, Oxidant Balance and Stress Signalling (NIH/NIGMS P20 GM103542). We dedicate this work to C.C.B.

Author information

Authors and Affiliations

Authors

Contributions

D.J.R., T.Y. and Y.M. conceived the study with assistance from D.R.M.; D.J.R., Y.L., C.M.K., B.D. and J.Y. designed the experiments with C.C.B., H.Y., T.Y. and Y.M.; D.J.R. supervised all of the experiments, led the data analyses and manuscript preparation with Y.M. Diffusion modelling was performed by R.C.C. and J.Y.; D.J.R. performed all of the immunofluorescence staining, confocal imaging and image analysis with assistance from J.J. and C.M.K.; D.J.R. performed all of the RNA-seq and microarray analysis of organoid and public datasets, including meta-analysis, PCA and GSEA. R.W., E.S.H. and G.H. performed RNA-seq, quality control of RNA-seq output and managed Advaita Bio input. C.M.K., G.C.B. and C.C.B. performed Seahorse metabolic experiments and analysis. H.Y., B.D. and J.Y. designed micropipette aspiration apparatus. D.J.R., J.Y. and C.M.K. performed mechanical testing and analysis. Y.L., X.C., H.Y. and T.Y. developed the customized 2PLSM. D.J.R. and Y.L. performed all of the multi-photon and customized 2PLSM imaging and analysis. G.H., D.R.M., C.C.B., H.Y., T.Y. and Y.M. supervised the efforts, including the manuscript preparation.

Corresponding authors

Correspondence to Tong Ye or Ying Mei.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary figures and legends for Supplementary Videos 1–10 and Supplementary Tables 1–7.

Reporting Summary

Supplementary Table 1

GO terms and P values for the overlapping regions of the Venn diagrams of DE genes in mice, pigs, humans and human cardiac organoids with ischaemic cardiac injury.

Supplementary Table 2

Top 35 GO terms.

Supplementary Table 3

Metabolic-pathway gene sets.

Supplementary Table 4

Fibrosis-related gene sets.

Supplementary Table 5

Significant P values in the radial-density plots of Figs. 4f, 6b and 7c.

Supplementary Table 6

Calcium-handling-related gene sets.

Supplementary Table 7

P values from the two-way ANOVA with post hoc Tukey tests in Fig. 7e.

Supplementary Video 1

Customized two-photon scanned light-sheet microscopy of more than 50 μm below the surface of the organoid for control organoids at day 10.

Supplementary Video 2

Customized two-photon scanned light-sheet microscopy of more than 50 μm below the surface of the organoid for infarct organoids at day 10.

Supplementary Video 3

Bright-field observations of synchronized control organoids at day 10.

Supplementary Video 4

Bright-field observations of unsynchronized infarct organoids at day 10.

Supplementary Video 5

Customized two-photon scanned light-sheet microscopy of more than 50 μm below the surface of the spheroid for infarct CM spheroids at day 10.

Supplementary Video 6

Customized two-photon scanned light-sheet microscopy of more than 50 μm below the surface of the organoid for control organoids at day 10 (derived from cells for donor B).

Supplementary Video 7

Customized two-photon scanned light-sheet microscopy of more than 50 μm below the surface of the organoid for infarct organoids at day 10 (derived from cells for donor B).

Supplementary Video 8

Bright-field observations of synchronized control organoids at day 10 (derived from cells for donor B).

Supplementary Video 9

Bright-field observations of unsynchronized infarct organoids at day 10 (derived from cells for donor B).

Supplementary Video 10

Customized two-photon scanned light-sheet microscopy of more than 50 μm below the surface of the organoid for infarct organoids at day 10 (with treatment with an anti-fibrotic drug candidate).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Richards, D.J., Li, Y., Kerr, C.M. et al. Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity. Nat Biomed Eng 4, 446–462 (2020). https://doi.org/10.1038/s41551-020-0539-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-020-0539-4

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research